On a Gibbs sampler based random process in Bayesian nonparametrics

We define and investigate a new class of measure-valued Markov chains by resorting to ideas formulated in Bayesian nonparametrics related to the Dirichlet process and the Gibbs sampler. Dependent random probability measures in this class are shown to be stationary and ergodic with respect to the law of a Dirichlet process and to converge in distribution to the neutral diffusion model.

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Year of publication:	2009
Authors:	Favaro, Stefano ; Ruggiero, Matteo ; Walker, Stephen G.
Institutions:	Collegio Carlo Alberto, Università degli Studi di Torino
Subject:	Random probability measure \| Dirichlet process \| Blackwell-MacQueen Pólya urn scheme \| Gibbs sampler \| Bayesian nonparametrics

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Extent:	application/pdf
Series:	Carlo Alberto Notebooks.
Type of publication:	Book / Working Paper
Language:	English
Notes:	Number 133 12 pages
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10008518900